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Abstract

Background

Controversy remains regarding the relationship between ambient ozone and
mortality worldwide. In mainland China, the largest developing country, there
has been no prior study investigating the acute effect of O3 on death risk. Given the changes in types of air pollution from conventional
coal combustion to the mixed coal combustion/motor vehicle emissions
in China’s large cities, it is worthwhile to investigate
the acute effect of O3 on mortality outcomes in the country.

Objectives

We conducted a time-series study to investigate the relation between O3 and daily mortality in Shanghai using 4 years of daily data (2001–2004).

Methods

We used the generalized additive model with penalized splines to analyze
mortality, O3 pollution, and covariate data in warm and cold seasons. We considered
daily counts of all-cause mortality and several cause-specific subcategories (respiratory
and cardiovascular). We also examined these associations
among several subpopulations based on age and sex.

Results

O3 was significantly associated with total and cardiovascular mortality in
the cold season but not in the warm season. In the whole-year analysis, an
increase of 10 μg/m3 of 2-day average (lag01) O3 corresponds to 0.45% [95% confidence interval (CI), 0.16–0.73%], 0.53% (95% CI, 0.10–0.96%), and 0.35% (95% CI, −0.40 to 1.09%) increase of total nonaccidental, cardiovascular, and
respiratory mortality, respectively. In the cold season, the
estimates increased to 1.38% (95% CI, 0.68–2.07%), 1.53% (95% CI, 0.54–2.52%), and 0.95% (95% CI, −0.71 to 2.60%), respectively. In
the warm season, we did not observe significant
associations for both total and cause-specific mortality. The results
were generally insensitive to model specifications such as lag structure
of O3 concentrations and degree of freedom for time trend. Multipollutant models
indicate that the effect of O3 was not confounded by particulate matter ≤ 10 μm in diameter (PM10) or by sulfur dioxide; however, after adding nitrogen dioxide into the
model, the association of O3 with total and cardiovascular mortality became statistically insignificant.

Conclusions

O3 pollution has stronger health effects in the cold than in the warm season
in Shanghai. Our analyses also strengthen the rationale for further
limiting levels of O3 pollution in outdoor air in the city.

Keywords: air pollution, mortality, ozone, time-series studies

Short-term exposure to outdoor air pollution has been linked to adverse
health effects, including increased mortality, increased rates of hospital
admissions and emergency department visits, exacerbation of chronic
respiratory conditions (e.g., asthma), and decreased lung function (American Thoracic Society 1996; Dockery and Pope 1994). Most of these studies were conducted in the developed countries, and
only a small number of studies have been conducted in Asia (Health Effects Institute 2004). In mainland China, the largest developing country, the relation between
outdoor air pollution and daily mortality has been investigated in
several large cities, including Beijing (Xu et al. 1994), Shenyang (Xu et al. 2000), Chongqin (Venners et al. 2003), and Shanghai (Kan and Chen 2003a, 2003b). These studies basically followed the commonly used time-series and case-crossover
approaches, and their results were in accordance with those
reported from Western Europe and the United States, where most epidemiologic
studies were conducted.

China has one of the world’s worst levels of ambient air pollution. Coal
has been the major source of energy in the country, constituting
about 75% of all energy sources. Consequently, air pollution
in China predominantly consists of coal smoke, with suspended particulate
matter (PM) and sulfur dioxide as the principal air pollutants. However, with
the rapid increase in the number of motor vehicles in
recent years, air pollution in China’s large cities has gradually
changed from the conventional coal combustion type to mixed coal combustion/motor
vehicle emissions (Chen et al. 2004). Given the relatively high levels of copollutants (PM, SO2, and nitrogen dioxide) and change of air pollution type in China’s
cities, it is worthwhile to investigate the independent effect of
O3 on mortality outcomes in the country. Moreover, in setting air pollution
control policy from a public health viewpoint, it is important to identify
the health effects of air pollution from local data.

In the present study, we conducted a time-series analysis to evaluate the
association between mortality outcomes (both total and cause specific) and
O3 exposure in metropolitan Shanghai using 4 years of daily data (2001–2004).

Materials and Methods

Data

Daily mortality data (excluding accidents and injuries) of residents living
in the nine urban districts of Shanghai from 1 January 2001 to 31 December 2004 were
collected from the database of the Shanghai Municipal
Center of Disease Control and Prevention. The causes of death for 2001 and 2002–2004 were coded according to the International Classification of Diseases, Ninth Revision [ICD-9; World Health Organization (WHO) 1977] and Tenth Revision [ICD-10 (WHO 1994)], respectively. The mortality data were classified into deaths
from all causes (ICD-9 codes < 800; ICD-10 codes A00–R99), cardiovascular
diseases (ICD-9 codes 390–459; ICD-10 codes I00–I99) (including
subcategories such as stroke and heart diseases), and
respiratory diseases (ICD-9 codes 460–519; ICD-10 codes
J00–J98) [including subcategories such as chronic
obstructive pulmonary disease (COPD) and acute respiratory infection]. The
data were also classified by sex and age (0–4, 5–44, 45–64, ≥ 65 years) for all-cause deaths.

Daily air pollution data in 2001–2004, including O3, PM ≤ 10 μm in diameter (PM10), SO2, and NO2, were collected by the Shanghai Environmental Monitoring Center. The daily
concentrations for each pollutant were averaged from the available
monitoring results of six fixed-site stations under China National Quality
Control located in the urban areas of Shanghai. We collected the 24-hr
average concentrations for PM10, SO2, and NO2 and 8-hr (1000 hr to 1800 hr) average concentration for O3. We used the 8-hr average because it is the average time recommended by
the World Health Organization (WHO) for reflecting the most health-relevant
exposure to O3 (WHO 2000). Calculation of 24-hr average concentration of PM10, SO2, and NO2 required having at least 75% of the 1-hr values on that particular
day. For the 8-hr average of O3, at least six hourly values from 1000 hr to 1800 hr had to be available. If
a station had > 25% of the values missing for the whole
period of analysis, the entire station was excluded from the analysis.

To allow adjustment for the effect of weather on mortality, daily (minimal, maximal, and
average) temperature and humidity data were collected
by the Shanghai Meteorological Bureau. The weather data were measured
at a fix-site station located in Xuhui District of Shanghai.

All the mortality, pollutant, and meteorological data were validated by
an independent auditing team assigned by the Health Effects Institute.

Statistical methods

We used the generalized additive model (GAM) with penalized splines to
analyze the mortality, O3 pollution, and covariate data from 2001 to 2004 in Shanghai. Because counts
of daily mortality data typically follow a Poisson distribution, the
core analysis was a GAM with log link and Poisson error that accounted
for smooth fluctuations in daily mortality.

We first built the basic models for various mortality outcomes that did
not include the air pollution variables. We incorporated smoothed spline
functions of time and weather conditions, which can accommodate nonlinear
and nonmonotonic patterns between mortality and time/weather conditions, offering
a flexible modeling tool (Hastie and Tibshirani 1990). According to previous literature (Bell et al. 2004; Samet et al. 2000a, 2000b), 6 or 8 degrees of freedom (df) per year of data for time trend and 3 or 4 df (whole
period of study) for temperature and relative humidity
were tested. This number of degrees of freedom has been found to control
well for seasonal patterns in mortality and to reduce, and often eliminate, autocorrelation. If there was over-dispersion in the variance, we
used the partial autocorrelation function (PACF) to guide the selection
of degrees of freedom until PACF of the residuals was < 0.1 for
the first 2 lag days (independent of the associated p-values). In this way, we determined the optimal degree-of-freedom values
per year for various causes of death in Shanghai. Other covariates, such
as day of the week (DOW), were also included in the basic models. Residuals
of each model were examined to check whether there were discernible
patterns and autocorrelation by means of residual plots and
PACF plots, respectively.

After the establishment of basic models, we introduced the pollutant variables
into the models and analyzed their effects on mortality outcomes. Generalized
cross-validation scores were used to compare the relative
quality of the mortality predictions across these non-nested models
and how well the models fit the data (Golub et al. 1979; Hastie and Tibshirani 1990).

Briefly, we fit the following log-linear GAM to obtain the estimated pollution
log-relative rate β in Shanghai:

where E(Yt) is the expected number of deaths at day t; β represents the log-relative rate of mortality associated with
a unit increase of air pollutants (O3 and copollutants); Zt indicates the pollutant concentrations at day t; DOW is day of the week effect; ps(time,df) is the penalized spline function
of calendar time; and ps(temperature/humidity,3) is the penalized
spline function for temperature/humidity with 3 df.

In a previous study, Bell et al. (2004) reported that single-day lag models under-estimate the cumulative effect
of O3 on mortality because they take into account only 1 day’s O3 exposure. Therefore, in our primary analysis, we used the moving average
of current and previous day concentrations of O3 (lag01). As a sensitivity analysis, we also examined the effect of O3 with different lag structures including both single-day lag and multiday
lag [lag01 and the 5-day moving average of O3 concentrations for the previous 4-days (lag04)]. We examined the
separate effect of O3 in the warm season (April–September) and the cold season (the
remaining months). In addition, given that it is not easy to determine
the optimal values of degrees of freedom for time trend in the basic
models whether from statistical or biological plausibility perspectives, we
did the sensitivity analysis to test the impact of degree of freedom
selection on the regression results. We also fitted models with a
different combination of pollutants (up to two pollutants per model) to
assess the stability of O3’s effect.

All analyses were conducted using R 2.1.1 using the MGCV package (R Development Core Team 2006). The results are presented as the percent change in daily mortality per 10μg/m3 increase of O3, which is simply the β-coefficient from the Poisson regression × 1,000.

Results

From 2001 to 2004, a total of 173,911 deaths (91,314 males and 82,597 females) were
recorded in the study population. The four different age
groups (0–4, 5–44, 45–64, and ≥ 65 years) accounted
for 0.3, 3.2, 13.0, and 83.5% of the total number
of deaths, respectively. On average, there were 119.0 deaths/day, among
which 44.2 persons died from cardiovascular diseases and 14.3 died
from respiratory diseases (Table 1). Cardiopulmonary disease accounted for 49.1% of the total nonaccidental
deaths for the urban residents in Shanghai.

In our research period, the minimal, mean, and maximal daily 8-hr average
concentrations of O3 were 5.3, 63.3, and 251.3 μg/m3, respectively (Table 1). For the cold and warm seasons, the mean 8-hr average O3 concentrations were 48.3 and 78.4 μg/m3, respectively.

The 8-hr average concentrations of O3 were weakly correlated with daily concentrations of PM10, SO2, and NO2 and moderately correlated with mean temperature level (Table 2). PM10, SO2, and NO2 had relatively higher correlation coefficients with each other.

Table 3 summarizes the effect of O3 on daily mortality stratified by cause of death and season in the single-pollutant
models. O3 was significantly associated with total and cardiovascular mortality in
the cold season but not in the warm season. In the whole-year analysis, an
increase of 10 μg/m3 of 2-day average (lag01) O3 corresponds to 0.45% [95% confidence interval (CI), 0.16–0.73%], 0.53% (95% CI, 0.10–0.96%), and 0.35% (95% CI, −0.40to 1.09%) increase of total, cardiovascular, and respiratory
mortality, respectively. In the cold season, the estimates increased
to 1.38% (95% CI, 0.68–2.07%), 1.53% (95% CI, 0.54–2.52%), and 0.95% (95% CI, −0.71 to 2.60%), respectively. In
the warm season, we did not observe significant associations for
both total and cause-specific mortality.

Percent increase (95% CI) of mortality outcomes of Shanghai residents
associated with a 10-μg/m3 increase in O3 concentrations in 2001–2004.a

For total nonaccident mortality, the estimated effect varied with sex and
age groups (Table 3). The observed effect of O3 was larger in females than in males. For people < 65 years of age, the
effects were not statistically significant, whereas for older urban
residents (≥ 65 years of age), the level of O3 concentrations was positively associated with mortality risk.

For total and cardiovascular mortality, the exposure–response relationships
associated with O3 exposure were essentially linear at concentrations < 75 μg/m3, although the risks were not monotonically increasing (Figure 1). The curves tended to become nonlinear and flat at higher concentrations. We
did not observe any obvious threshold concentration below which
O3 has no effect on total and cardiovascular deaths. For respiratory mortality, no
clear relationship was observed.

In our analysis, the effects of O3 on total and cardiovascular mortality are statistically significant for
most lagged days that we examined (Figure 2). For single-day lags, O3 shows similar patterns for its effects on the mortality outcomes in that
the risks increased from lag day 0, were maximal at lag days 1–2, and
then declined. Multiday exposures (lag01 and lag04) usually
have larger effects than single-day exposure. The effect of O3 on respiratory mortality was only significant for single-day lag 2.

Percent increase of deaths with 10 μg/m3 increase of O3 due to all, cardiovascular, and respiratory causes in different lag days.

Within the range of 5–15 df, the change of degrees of freedom per
year for time trend does not much affect the regression results (Figure 3), suggesting that our findings with regard to the effect of O3 on mortality outcomes are relatively robust.

Table 4 compares the results of the single-pollutant models and two-pollutant
models. The estimated effects of O3 on total and cardiovascular mortality were still significant after adjustment
for PM10 and SO2; however, NO2 was added into the regression models, the effect of O3 became statistically insignificant. We did not observe significant effects
of O3 on respiratory mortality in single-pollutant or two-pollutant models.

Percent increase of total, cardiovascular, and respiratory mortality associated
with a 10-μg/m3 increase of 2-day average O3 concentrations under single- and two-pollutant models.a

Discussion

Evidence gained in this study showed that the current level of O3 in Shanghai is associated with the death rates from all causes and from
cardiovascular diseases in the cold season. To our knowledge, this is
the first study to report the acute effect of O3 exposure on daily mortality in mainland China. Our results should contribute
to the understanding of O3-related health effects in China and may help clarify the difference in
effects and mechanisms of O3 between Western and Eastern populations.

Our analysis indicates an association between short-term change in O3 and mortality, with an estimated 0.45% increase in total mortality (95% CI, 0.16–0.73%) for a 10-μg/m3 increase in the 8-hr average O3 level at lag01 in the whole-year analysis. To compare this estimate with
other studies, all estimates must be based on the same measure of O3 concentration, such as the 8-hr average. Most previous meta-analyses and
time-series analyses used 1-hr maximal, 8-hr maximal, or daily (24-hr) average
concentrations as O3 exposure metrics (Bell et al. 2004, 2005; Ito et al. 2005; Levy et al. 2005). A recent study of 23 European cities found a 0.34% (95% CI, 0.27–0.50) increase in daily all-cause mortality associated
with a 10-μg/m3 increase in the average of the daily 8-hr average of the same and previous
days (Gryparis et al. 2004), which is roughly comparable to our estimate. The magnitude of our estimate
is also comparable to another study conducted in Hong Kong using 8-hr
average O3 concentrations (Wong et al. 2001).

A major finding of the present study was significant effects of O3 on mortality outcomes only in the cold season but not in the warm season. This
is consistent with two prior studies in Hong Kong (Wong et al. 1999, 2001) but in contrast to most studies in Western countries (Bell et al. 2004, 2005; Ito et al. 2005; Schwartz 2005). In Shanghai, the O3 level was higher in the warm season than in the cold season (mean level, 78.4 μg/m3 vs. 48.3 μg/m3), and our exposure–response relationship also reveals a flatter
slope at higher concentrations (Figure 1). At higher concentrations, the risks of death could be reduced because
vulnerable subjects may have died before the concentration had reached
the maximum level (Wong et al. 2001). In addition, the exposure pattern may also contribute to our observation. During
the warm season, Shanghai residents tend to use air conditioning
more frequently because of the relatively higher temperature and
humidity, thus reducing the risk of outdoor O3 exposure. Unstable weather conditions (heavy rain and rain storms) in
the warm season also prevent the acute exposure–response relationships
between O3 and mortality from being readily observable. In contrast, the cool season
in Shanghai is drier and less variable, so people are more likely
to go outdoors and open the windows. The fact that a consistently significant
health effect of O3 was observed only in the cold season in two subtropical Asian cities (Shanghai
and Hong Kong) suggests that the interaction of O3 exposure and weather pattern may vary by location and should be further
investigated.

For total nonaccident mortality, we found a larger effect of O3 in females than in males. In Shanghai, male residents have a much higher
smoking rate than do females (50.6% and 0.6%, respectively) (Xu 2005). A previous study (Künzli et al. 2005) suggested that the air pollution effect may be stronger in nonsmokers
than in smokers. Oxidative and inflammatory effects of smoking may dominate
to such an extent that the additional exposure to O3 may not further enhance effects along the same pathways. In addition, compared
with males, females have slightly greater airway reactivity (Yunginger et al. 1992); therefore, it is possible that dose–response relations may be
detected more easily in females than in males.

Our study area—nine urban districts of Shanghai—is densely
populated. Within an area of 279 km2, there are around 7 million permanent residents and six China National
Quality Control monitoring stations providing the exposure data for this
study. In addition, compared with the residents in developed countries, a
relatively lower proportion of Shanghai residents have access
to or use air conditioning. Thus, the monitored ambient air pollution
data might have been more closely associated with average population exposures
in Shanghai than in other study locations of developed countries.

The limitations of our exposure assessment should also be noted. As in
most previous time-series studies, we used the simply averaged monitoring
results across various stations as the proxy of population exposure
level to air pollution. That assignment method may raise a number of
issues, given that the variance of pollutant measurements can differ
from monitoring location to monitoring location and given the difference
between ambient monitoring results and personal exposure level to O3. In addition, because O3 is highly reactive in indoor environments (where people spend most of
their time) (Zhang and Lioy 1994), ambient O3 concentrations tend to be higher than personal O3 exposures (Avol et al. 1998). These influences challenge the accuracy of our exposure assessment and
the following time-series analysis. The resulting measurement error
may have substantial implications for interpreting the time-series air
pollution studies (Zeger et al. 2000), although a study has suggested that this measurement error would generally
tend to bias estimates downward (Samet et al. 2000b). In the future, we hope to develop an algorithm that fits the local characteristics
and can be used to estimate the aggregate population exposure
level to various pollutants in Shanghai.

In the single-day lag models, the estimated effects of O3 on mortality outcomes reached a maximum at a lag of 1–2 days. Multiday
exposure (e.g., lag01 and lag04) models generally produced larger
estimates compared with the single-day lag models (Figure 2). These observations are consistent with those of previous air pollution
health effects reports (Bell et al. 2004; Braga et al. 2001; Zanobetti et al. 2002). This temporal pattern of effect would be anticipated for O3, which produces acute inflammatory responses in the lung; adaptation of
this inflammatory response with several days of repeated exposure has
been demonstrated (Folinsbee et al. 1994; Frank et al. 2001). Although the temporal dynamics of the underlying processes linking O3 exposure to increased mortality may differ from those of the inflammatory
response, inflammation has been postulated as having a central role
in the increased mortality and morbidity associated with O3 (Brook et al. 2004).

In real life, people cannot selectively inhale some air pollutants and
not others. Therefore, human health effects may be the result of a complex
of inhaled multipollutants, and it is very difficult to separate
the effect of individual pollutants. In the present analysis, the concentration
of O3 was weakly correlated with other pollutants. This lack of correlation
and the stability of the O3 estimate with inclusion of PM10 and SO2 in the multipollutant models provide evidence against confounding of the
effects of other pollutants. However, our estimate of O3 on total and cardiovascular mortality became statistically insignificant
after adding NO2 into the model. Our observed effect of O3 may actually reflect the risk from the photochemical pollution mixture
more generally. In addition to O3, atmospheric photochemistry produces several hazardous pollutants, such
as peroxyacyl nitrates. O3 may act as a surrogate indicator for this highly complex and geographically
variable mixture and is likely to be an imperfect measure of potential
toxicity (Bell et al. 2004).

Our estimated effect was relatively robust to the confounding factors such
as seasonality, long-term trends, temperature, and other pollutants. The
results indicate a substantial health burden from O3 pollution. However, this value is probably an underestimate of the total
mortality burden from such an increase in O3 because it accounts for only the short-term effects. Further, we found
a relationship between mortality and O3 at pollution levels below the current regulatory standard. Our analysis
is limited to the urban area of Shanghai, although rural communities
may also experience elevated O3 levels, especially because of large biogenic emissions of volatile organic
compounds and the movement of O3 and O3 precursors from urban regions.

Several groups within the population have been considered at increased
risk from O3 exposure, including women, older persons, and those with underlying chronic
cardiovascular diseases. Our study also confirmed the previous findings
that the association between O3 exposure and the mortality risk of cardiovascular diseases was stronger
than all-cause mortality risk. There are several possible underlying
mechanisms for the possible link between O3 exposure and cardiovascular mortality: inflammation of pulmonary tissues, which
can induce a spectrum of mediators that also may alter cardiac
functions, or irritant-receptor–mediated stimulation of parasympathetic
pathways (Watkinson et al. 2001). O3 is a potent oxidant that has been shown to produce free radicals and oxidative
stress on lung cells (Ahmad et al. 2005); however, we did not observe a significant effect of O3 on respiratory mortality. This is consistent with the results of a recent
meta-analysis from 39 time-series studies (Bell et al. 2005). The relative small number of deaths due to respiratory diseases may
have limited our ability to detect small pollution association (Kinney and Ozkaynak 1991).

Conclusion

The results presented here show an independent association between mortality
outcomes and O3 exposure in the cold season in Shanghai. Our analyses provide evidence
that the current level of O3 has an adverse effect on the health of the general population and strengthen
the rationale for further limiting levels of O3 pollution in outdoor air in Shanghai.

Footnotes

This study was funded by the Health Effects Institute through grant 4717-RFIQ03-3/04-13. The
research was also supported by the Division of Intramural
Research, NIEHS.

The views expressed in this article are those of the authors and do not
necessarily reflect the views of the Health Effects Institute or its
sponsors.

Bell ML, Dominici F, Samet JM. A meta-analysis of time-series studies of ozone and mortality with comparison
to the national morbidity, mortality, and air pollution study. Epidemiology. 2005;16:436–445.[PMC free article][PubMed]